Opportunities from multi-dimensional crop
improvement and the supporting role of Near
Infrared Spectroscopy(NIRS) networks
Michael Blümmel
International Livestock Research Institute
11 November 2016

Topics
Why pay attention to crop residues: feed supply-demand
scenarios, context, fodder markets
 Impact of differences in crop residue fodder quality on livestock
productivity
 Exploit existing cultivar variations and targeted genetic
enhancement, trade-offs
 NIRS hubs as logistical support infrastructure for phenotyping in
multidimensional crop improvement

Feed resource supply - demand scenarios in
India
Feed resource Contribution to overall feed resources (%)
Greens from CRP, forests, grazing 8.0
Planted forages 15.1
Crop residues 70.6
Concentrates 6.3
Deficit: feed availability versus feed requirement (%)
Dry matter (i.e. crop residue quantity) -6
Digestible crude protein -61
Total digestible nutrients -50
(NIANP 2012; Blümmel at al. 2014)

0
5 0 0
1 0 0 0
1 5 0 0
2 0 0 0
2 5 0 0
3 0 0 0
3 5 0 0
4 0 0 0
4 5 0 0
Yield differences in milk production between the 10%most productive
farmers and the remaining 90% in India when managing
comparable dairy genetics
(Derived from VDSA-India 2013 and Blümmel et al. 2016b)
A ll C o w s
L o c a l C o w s
C ro s s b re d C o w s
1 0 %
1 0 %
1 0 %
9 0 %
9 0 %
9 0 %
G A P : 2 8 1 7 k g G A P : 8 6 9 k g G A P : 2 3 0 9 k g
AverageMilkYield(kg/cow/year)

Crop residues are becoming more
important
Kahsay Berhe (2004) study in Yarer Mountain area
 Cultivated land has doubled at the expense of
pasture in 30 years
 Switch in source of nutrition for livestock from
grazing to CR

4 0 5 0 6 0 7 0 8 0 9 0
0
1 0 0
D ry m a tte r in ta k e
D ig e s tib le e n e rg y in ta k e
P h y s ic a l P h y s io lo g ic a l
F o d d e r q u a lity : h e re D ig e s tib ility (% )
IncreasingIntake
S e t p o in t
R e la tio n s h ip b e tw e e n fo d d e r q u a lity
a n d v o lu n ta ry fe e d in ta k e

Sorghum stover trading in Hyderabad
7

Nov Dec Jan Feb Mar Apr May Ju Jul Aug Sep Oc Nov
0
2
4
6
8
10
12
14
Sorghum grain
Sorghum stover
3.4
6.5
Month of trading
IndianRupeeperkg
Yearly mean
2004 to 2005
Nov Dec Jan Feb Mar Apr May Ju Jul Aug Sep Oc Nov
0
2
4
6
8
10
12
14
Sorghum stover
Sorghum grain
6.2
10.2
Yearly mean
2008 to 2009
Month of trading
Comparisions of average cost of dry sorghum stover traded in Hyderabad and average of cost of
sorghum grain in Andhra Pradesh 2005 to 2005 and 2008 to 2009
Changes in grain: stover value in sorghum
traded in Hyderabad from
2004-5 to 2008-9
Sharma et al. 2010

Relation between digestibility and
price of sorghum stover
44 45 46 47 48 49 50 51 52 53 54 55
2.8
3.0
3.2
3.4
3.6
3.8
4.0
4.2
y = -4.9 + 0.17x; R2
= 0.75; P = 0.03
Stover in vitro digestibility (%)
Stoverprice(IR/kgDM)
Premium Stover
“Raichur”
Low Cost Stover
“Local Yellow”
Blümmel and Parthasarathy, 2006

Price variations in different sorghum stover traded
concomitantly in Mieso in April 2007
Stover
ETB/kg
Trader
ETB/kg
Farm
Sweet Sorghum (SS) 0.65 0.20
“Grain” Sorghum (GS) 0.50 0.13
Price premium 30% 54%
Source: calculated from Gebremedhin et al. 2009
Note: In India SS stover have about 3-4 units higher digestibility than GS stover

Price: quality relations in rice straw traded
monthly in Kolkata from 2008 to
2009
37.0 37.5 38.0 38.5 39.0 39.5 40.0 40.5 41.0 41.5 42.0
2.75
3.00
3.25
3.50
3.75
4.00
4.25
Best (n=81)
Good (n=260)
Medium/low
(n=273)
In vitro digestibility of rice straw (%)
PriceofricestrawatKolkatatraders
from2008-2009(IndianRupees/kg)
Teufel et al. (2012)

Conclusions: why pay attention to crop
residues as fodder
 Feed supply – demand scenarios underline key role
of crop residues as feed sources
 Fodder market surveys show high monetary value,
narrowing crop residue-grain price ratios
 Driving factor: more quality fodder required with
shrinking natural resource basis

Impact of variations in crop residue fodder
quality on livestock productivity
 Effect of superior stover as basal diet
 What magnitude of fodder quality difference
matter and why
Livestock productivity levels on entirely crop residue
based diets

Feed block manufacturing: supplementation,
densification
Ingredients %
Sorghum stover 50
Bran/husks/hulls 18
Oilcakes 18
Molasses 8
Grains 4
Minerals, vitamins, urea 2
Courtesy: Miracle Fodder and Feeds PVT LTD

Relation between digestibility and
price of sorghum stover
44 45 46 47 48 49 50 51 52 53 54 55
2.8
3.0
3.2
3.4
3.6
3.8
4.0
4.2
y = -4.9 + 0.17x; R2
= 0.75; P = 0.03
Stover in vitro digestibility (%)
Stoverprice(IR/kgDM)
Premium Stover
“Raichur”
Low Cost Stover
“Local Yellow”
Blümmel and Parthasarathy, 2006

Comparisons of feed blocks based on lower (47%) and
higher (52%) digestible sorghum stover and tested
with commercial dairy buffalo farmer in India
Block Premium Block Low
CP 17.2 % 17.1%
ME (MJ/kg) 8.46 MJ/kg 7.37 MJ/kg
DMI 19.7 kg/d 18.0 kg/d
DMI per kg LW 3.8 % 3.6 %
Milk Potential* 15.5 kg/d 9.9 kg/d
Modified from Anandan et al. (2009a)
* 21 and 14 kg/d in crossbred cattle

Live weight gains in Indian Deccan sheep
fed exclusively on groundnut haulms
Groundnut cultivars Gain (g/d)
ICGV 89104 137
ICGV 9114 123
TMV 2 111
ICGS 76 76
ICGS 11 76
DRG 12 66
ICGS 44 65
ICGV 86325 83
ICGV 92020 95
ICGV 92093 109
Prob > F 0.02
Prasad et al. 2010

Live weight changes in Ethiopian Arsi-Bale
sheep fed exclusively on Faba bean straws
Wegi et al., 2016
Cultivars Grain Yield Straw Yield Weight Gain (g/d)
Mosisa 4.28a 5.68a 52.2ab
Walki 4.21a 4.42c 64.6a
Degaga 4.20a 4.31c 43.2bc
Shallo 4.06a 4.98b 37.5c
Local 2.89b 3.65d 48.3bc

Conclusions: Impacts of variations in crop
residue fodder quality on livestock productivity
 “Intuitively” small difference in fodder quality of
stover do matter: additive effect of higher
diet quality and higher intake
 Informed choice of cultivar can have very
substantial effect on livestock
productivity

Exploit existing cultivar variations
 Phenotyping new cultivars submitted for release
testing for fodder traits
• Laboratory infrastructure: stationary NIRS, mobile
NIRS
 Phenotyping during crop improvement for fodder
traits

Stover fodder trait analysis in new sorghum cultivar release
testing in India 2002 to 2008
(Blümmel et al. 2010)

Cultivar CP IVOMD ME SY
% % MJ/kg kg/ha
ICSC 93046 7.6 58.8 8.87 15 814
ICSV 91005 7.5 58.3 8.75 17 734
ICSR 196 7.9 55.2 8.19 10 386
ICSR 56 6.9 54.8 8.23 9 698
NT J2 6.6 54.8 8.19 11 675
E-36-1 7.0 53.7 8.00 9 256
ICSR 93034 6.6 53.6 8.00 10 176
A 2267-2 6.3 52.6 7.82 10 046
Seredo 6.1 52.6 7.86 8 069
ICSV 96143 6.6 51.8 7.64 6 295
WSV-387 6.5 51.7 7.64 7 911
ICSV 111 5.9 50.8 7.56 7 372
Statistical summary
LSD 0.5 1.6 0.25 1 726
h2 0.65 0.49 0.50 0.47
South-south transfer of superior dual purpose sorghum cultivars:
tested 2 years x 3 locations in Ethiopia
Adie et al. 2016

Conclusions: exploit existing cultivar
variations
 Livestock nutritionally-significant variations exist in
all key cereal and legume crops (except perhaps
wheat)
 Short impact pathways, quick, relatively little
investment
 Modifying cultivar release criteria promising entry
point

Targeted genetic enhancement
Targeted genetic enhancement towards dual and
multi purpose traits
 Conventional breeding (recurrent selection,
hybridization)
 Molecular breeding (QTL introgression, Genetic
selection)

Response in stover in vitro digestibility to 2
cycles of selection of pearl millet
variety ICMV 221
Digestibility
%
Grain Yield
kg/ha
Stover yield
kg/ha
Original 43.6 2 669 3 095
H1 44.5 2 596 3 460
L1 42.1 2 592 2 889
H2 45.8 2 564 3 168
L2 42.0 2 408 2 731
25
Choudhary et al (in preparation )

Effect of introgression of different stay green QTL’s
on stover digestibity of a Rabi
sorghum background
(Blümmel at al. 2015)

(Blümmel at al. 2015)

Dual Purpose Maize: Genomic Selection
(Babu et al. 2016)
DTMA & CAAM New DH Lines

ID IVOMD - Predicted IVOMD-Observed
DH_9_157 High IVOMD and ME 57.1
DH_3_33 High IVOMD and ME 56.7
DH_3_63 High IVOMD and ME 55.8
DH_9_15 High IVOMD and ME 55.7
DH_8_4 High IVOMD and ME 55.6
DH_3_149 High IVOMD and ME 55.5
DH_3_24 High IVOMD and ME 55.4
DH_6_1 Low IVOMD and ME 55.4
DH_3_10 High IVOMD and ME 55.0
DH_3_21 High IVOMD and ME 54.9
DH_3_138 High IVOMD and ME 54.6
DH_3_35 High IVOMD and ME 54.5
DH_3_61 High ME 54.4
DH_3_83 High IVOMD and ME 54.1
DH_9_165 High IVOMD 53.6
DH_9_134 High IVOMD 53.6
DH_9_153 High IVOMD and ME 53.5
DH_3_47 High IVOMD and ME 53.4
DH_3_62 High IVOMD and ME 53.4
DH_3_87 High IVOMD and ME 53.4
DH_3_82 High IVOMD 53.3
Predicting performance of DH maize lines for fodder quality
HTMA - GS
Pred.
Accuracy
IVOMD 0.44
ME 0.45
(Babu et al.2016)

4 5 .0 4 7 .5 5 0 .0 5 2 .5 5 5 .0 5 7 .5 6 0 .0
2 .8
3 .0
3 .2
3 .4
3 .6
3 .8
4 .0
4 .2
S to v e r in v itro digestibility (% )
Stoverprice(IR/kgDM)
B re e d in g a d v a n c e in d u a l p u rp o s e m a iz e s to v e r fo d d e r q u a lity
re la tiv e to d iffe re n t s o rg h u m s to v e r tra d e d in ra in fe d
In d ia in th e p a s t d e c a d e
L o w q u a lity
s o rg h u m sto ve r
H ig h q u a lity
s o rg h u m sto ve r
M e a n IV O M D (ra n g e 5 5 .2 to 5 7 .9 % )
o f 1 1 a d v a n c e d d u a l p u rp o s e m a ize
b re e d in g lin e s g e n e ra te d
d u rin g th e p ro je c ts
M e a n IV O M D (ra n g e 5 3 .6 to 5 6 .0 % )
o f 1 1 e xp e rim e n ta l h e a t to le ra n t d u a l
p u rp o s e m a iz e h y b rid s g e n e ra te d
d u rin g th e p ro je c ts
B lü m m e l e t a l. (2 0 1 6 a )

Conclusions: targeted genetic
enhancement
 Longer term, higher investments
 Great impact opportunities
 Multi-trait options
 New tools becoming available and more
affordable

Required infrastructure for
phenotyping for crop residue fodder
quality
 Stationary Near Infrared Spectroscopy (NIRS)
 Mobile NIRS

Qualitative trait prediction in plant breeding based
on Near Infrared Spectroscopy (NIRS)
Non-evasive
c. 200 samples/d
>30 traits
Physico-chemical
c. 60 000 US $
Calibration
Validation
NIRS equations sharable across
compatible instruments
At current: ILRI

34
Transfer of NIRS equations for phenotyping for fodder
quality traits: example cowpea
ILRI FOSS 5000
ILRI FOSS 6500

Methionine Prediction by NIRS

Mobile handheld NIRS
• About 40 000 US$ but price
decreasing
• Application currently developed and validated
at ILRI India and Ethiopia
Luminar 5030
Phazir

Conclusions: NIRS infrastructure for phenotyping
in multidimensional crop improvement
 Multi-dimensional crop improvement can be
mainstreamed using NIRS
 NIRS hubs minimize new investments and optimze
older ones (South Asia, East Africa, West Africa)
 Sample grinding the real bottleneck and rate limiting
procedure
 Mobile NIRS a way out?

Where to go from here
 Develop the East African NIRS hub based on ILRI-
EIAR-Private Sector collaboration
 Screen released and pipeline key cereal and
legumes crops for food-feed-fodder traits
 Explore feed and fodder value chains around
improved crop residues

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